# uamcf/methods/pruning.py
import numpy as np
from typing import Dict, Any, Optional, List, Tuple, Union

from .base import CompressionMethod


class Pruning(CompressionMethod):
    """实现权重剪枝的压缩方法"""

    def __init__(self, config: Optional[Dict] = None):
        """
        初始化剪枝方法

        Args:
            config: 剪枝配置参数，包括：
                - sparsity: 目标稀疏度 (float, 0-1之间, 默认: 0.5)
                - method: 剪枝方法 ('magnitude', 'structured', 'pattern', 默认: 'magnitude')
                - granularity: 剪枝粒度 ('element', 'kernel', 'channel', 'filter', 默认: 'element')
                - schedule: 剪枝调度 ('one-shot', 'iterative', 默认: 'one-shot')
        """
        super().__init__(config)
        self.sparsity = self.config.get("sparsity", 0.5)
        self.method = self.config.get("method", "magnitude")
        self.granularity = self.config.get("granularity", "element")
        self.schedule = self.config.get("schedule", "one-shot")

        self.logger.info(f"Initialized pruning with {self.sparsity * 100}% sparsity using {self.method} method")

    def compress(self, layer: Any) -> Any:
        """
        对层的权重进行剪枝

        Args:
            layer: 要剪枝的层

        Returns:
            剪枝后的层
        """
        self.logger.info(f"Applying {self.method} pruning with {self.sparsity * 100}% sparsity")

        try:
            # 这里的实现是框架无关的伪代码
            # 实际实现应由特定框架的适配器处理
            if hasattr(layer, "weight") and layer.weight is not None:
                # 记录原始权重用于评估
                self.original_weight = layer.weight.clone() if hasattr(layer.weight, "clone") else layer.weight

                # 根据选择的方法进行剪枝
                if self.method == "magnitude":
                    layer = self._magnitude_pruning(layer)
                elif self.method == "structured":
                    layer = self._structured_pruning(layer)
                elif self.method == "pattern":
                    layer = self._pattern_pruning(layer)
                else:
                    self.logger.warning(f"Unknown pruning method: {self.method}, using magnitude pruning")
                    layer = self._magnitude_pruning(layer)

                self.logger.info("Pruning applied successfully")
            else:
                self.logger.warning("Layer has no weights to prune")

            return layer

        except Exception as e:
            self.logger.error(f"Pruning failed: {str(e)}")
            # 出错时返回原始层
            return layer

    def _magnitude_pruning(self, layer: Any) -> Any:
        """基于权重绝对值大小的剪枝"""
        try:
            # 获取权重
            weights = layer.weight

            # 计算阈值
            if self.granularity == "element":
                # 将所有权重展平并排序
                flat_weights = np.abs(weights).flatten()
                threshold_idx = int(len(flat_weights) * self.sparsity)
                if threshold_idx < len(flat_weights):
                    threshold = np.sort(flat_weights)[threshold_idx]

                    # 创建掩码
                    mask = np.abs(weights) > threshold

                    # 应用掩码
                    pruned_weights = weights * mask
                    layer.weight = pruned_weights

                    # 存储掩码以便后续使用
                    layer.pruning_mask = mask

            elif self.granularity == "kernel":
                # 假设权重形状为 [out_channels, in_channels, kernel_h, kernel_w]
                # 对每个kernel进行单独剪枝
                for oc in range(weights.shape[0]):
                    for ic in range(weights.shape[1]):
                        kernel = weights[oc, ic]
                        flat_kernel = np.abs(kernel).flatten()
                        threshold_idx = int(len(flat_kernel) * self.sparsity)
                        if threshold_idx < len(flat_kernel):
                            threshold = np.sort(flat_kernel)[threshold_idx]
                            mask = np.abs(kernel) > threshold
                            weights[oc, ic] = kernel * mask

            elif self.granularity == "channel" or self.granularity == "filter":
                # 计算每个通道/过滤器的重要性
                importance = []

                if self.granularity == "channel":
                    # 对输入通道剪枝 (假设权重形状为 [out_channels, in_channels, ...])
                    for i in range(weights.shape[1]):
                        channel_weights = weights[:, i, ...]
                        importance.append((i, np.sum(np.abs(channel_weights))))
                else:
                    # 对输出通道/过滤器剪枝
                    for i in range(weights.shape[0]):
                        filter_weights = weights[i, ...]
                        importance.append((i, np.sum(np.abs(filter_weights))))

                # 根据重要性排序
                importance.sort(key=lambda x: x[1])

                # 确定要剪枝的通道/过滤器数量
                prune_count = int(len(importance) * self.sparsity)

                # 创建掩码
                mask = np.ones_like(weights, dtype=bool)

                # 将重要性最低的通道/过滤器置零
                for i in range(prune_count):
                    if self.granularity == "channel":
                        mask[:, importance[i][0], ...] = False
                    else:
                        mask[importance[i][0], ...] = False

                # 应用掩码
                layer.weight = weights * mask
                layer.pruning_mask = mask

            # 存储剪枝信息
            layer.pruning_info = {
                "sparsity": self.sparsity,
                "method": self.method,
                "granularity": self.granularity
            }

            return layer

        except Exception as e:
            self.logger.error(f"Magnitude pruning failed: {str(e)}")
            return layer

    def _structured_pruning(self, layer: Any) -> Any:
        """结构化剪枝实现"""
        try:
            # 结构化剪枝通常是对整个通道或过滤器进行剪枝
            weights = layer.weight

            # 计算每个输出通道/过滤器的L1范数
            filter_norms = []
            for i in range(weights.shape[0]):
                filter_weights = weights[i, ...]
                filter_norms.append((i, np.sum(np.abs(filter_weights))))

            # 根据L1范数排序
            filter_norms.sort(key=lambda x: x[1])

            # 确定要剪枝的过滤器数量
            prune_count = int(len(filter_norms) * self.sparsity)

            # 创建掩码
            mask = np.ones_like(weights, dtype=bool)

            # 将L1范数最小的过滤器置零
            for i in range(prune_count):
                mask[filter_norms[i][0], ...] = False

            # 应用掩码
            layer.weight = weights * mask
            layer.pruning_mask = mask

            # 存储剪枝信息
            layer.pruning_info = {
                "sparsity": self.sparsity,
                "method": self.method,
                "pruned_filters": [filter_norms[i][0] for i in range(prune_count)]
            }

            return layer

        except Exception as e:
            self.logger.error(f"Structured pruning failed: {str(e)}")
            return layer

    def _pattern_pruning(self, layer: Any) -> Any:
        """模式剪枝实现"""
        try:
            # 模式剪枝通常应用于卷积层，剪枝特定的权重模式
            weights = layer.weight

            # 这里使用一个简单的N:M稀疏模式示例
            # 例如4:8模式表示每8个权重中保留4个最大的
            n = 4  # 可以从配置中获取
            m = 8

            # 确保权重是4D的 [out_channels, in_channels, height, width]
            if len(weights.shape) != 4:
                self.logger.warning("Pattern pruning is typically applied to 4D conv weights")
                return layer

            # 将权重重新排列成能被m整除的块
            # 这里是简化实现，实际应更加复杂
            out_c, in_c, h, w = weights.shape
            weights_reshaped = weights.reshape(out_c, in_c, -1)

            # 对每个m个权重的块进行处理
            for oc in range(out_c):
                for ic in range(in_c):
                    vec = weights_reshaped[oc, ic, :]
                    for i in range(0, len(vec), m):
                        end = min(i + m, len(vec))
                        if end - i < m:  # 处理尾部不足m个的情况
                            continue

                        block = vec[i:end]
                        # 找出绝对值最大的n个元素
                        indices = np.argsort(np.abs(block))
                        # 将其余的置零
                        block[indices[:(m - n)]] = 0
                        vec[i:end] = block

            # 重新调整形状
            layer.weight = weights_reshaped.reshape(out_c, in_c, h, w)

            # 存储剪枝信息
            layer.pruning_info = {
                "sparsity": self.sparsity,
                "method": self.method,
                "pattern": f"{n}:{m}"
            }

            return layer

        except Exception as e:
            self.logger.error(f"Pattern pruning failed: {str(e)}")
            return layer

    def evaluate(self, original: Any, compressed: Any) -> Dict:
        """
        评估剪枝前后的层性能

        Args:
            original: 原始层
            compressed: 剪枝后的层

        Returns:
            性能评估指标
        """
        metrics = {}

        try:
            # 计算实际稀疏度
            if hasattr(compressed, "weight"):
                orig_w = original.weight
                comp_w = compressed.weight

                # 计算非零元素比例
                orig_nonzeros = np.count_nonzero(orig_w)
                comp_nonzeros = np.count_nonzero(comp_w)

                if orig_nonzeros > 0:
                    actual_sparsity = 1.0 - (comp_nonzeros / orig_nonzeros)
                    metrics["actual_sparsity"] = float(actual_sparsity)

                # 计算模型大小减少比例
                orig_size = orig_w.size * 32  # 假设原始为float32 (32位)

                # 稀疏矩阵通常需要额外存储索引
                indices_overhead = comp_nonzeros * (32 if self.granularity == "element" else 8)  # 索引开销
                comp_size = comp_nonzeros * 32 + indices_overhead

                if orig_size > 0:
                    metrics["compression_ratio"] = float(orig_size / comp_size)

            # 添加剪枝信息
            if hasattr(compressed, "pruning_info"):
                metrics["pruning_info"] = compressed.pruning_info

        except Exception as e:
            self.logger.error(f"Evaluation failed: {str(e)}")

        return metrics

    def can_apply(self, layer_type: str) -> bool:
        """检查剪枝是否适用于该层类型"""
        # 剪枝主要适用于卷积和全连接层
        applicable_types = [
            "Conv1d", "Conv2d", "Conv3d",
            "Linear", "Dense",
            "LSTM", "GRU"
        ]
        return layer_type in applicable_types

    @classmethod
    def get_default_params(cls) -> Dict:
        """获取默认剪枝参数"""
        return {
            "sparsity": 0.5,
            "method": "magnitude",
            "granularity": "element",
            "schedule": "one-shot"
        }

    def get_expected_metrics(self, layer_info: Dict) -> Dict:
        """
        获取剪枝的预期性能影响

        Args:
            layer_info: 层的信息

        Returns:
            预期的性能指标变化
        """
        # 根据稀疏度和剪枝方法估算精度影响
        if self.method == "magnitude" and self.granularity == "element":
            # 元素级别的幅度剪枝对精度影响通常较小
            if self.sparsity <= 0.5:
                accuracy_ratio = 0.98
            elif self.sparsity <= 0.7:
                accuracy_ratio = 0.95
            elif self.sparsity <= 0.9:
                accuracy_ratio = 0.9
            else:
                accuracy_ratio = 0.85

        elif self.method == "structured":
            # 结构化剪枝对精度影响较大
            if self.sparsity <= 0.3:
                accuracy_ratio = 0.97
            elif self.sparsity <= 0.5:
                accuracy_ratio = 0.93
            elif self.sparsity <= 0.7:
                accuracy_ratio = 0.85
            else:
                accuracy_ratio = 0.75

        else:
            # 其他方法
            accuracy_ratio = 1.0 - (self.sparsity * 0.2)

        # 计算大小减少比例（考虑稀疏存储开销）
        if self.method == "structured":
            # 结构化剪枝可以真正减少模型大小
            size_ratio = 1.0 - self.sparsity
        else:
            # 非结构化剪枝需要存储索引，减少效果不如稀疏度
            size_ratio = 1.0 - (self.sparsity * 0.7)

        # 剪枝对延迟的影响
        if self.method == "structured":
            # 结构化剪枝可以实际减少计算量
            latency_ratio = 1.0 - (self.sparsity * 0.8)
        elif self.granularity == "pattern":
            # N:M模式剪枝对硬件友好
            latency_ratio = 1.0 - (self.sparsity * 0.6)
        else:
            # 非结构化稀疏对大多数硬件加速不明显
            latency_ratio = 1.0 - (self.sparsity * 0.3)

        # 构建预期指标
        return {
            "accuracy_ratio": accuracy_ratio,
            "size_ratio": size_ratio,
            "latency_ratio": latency_ratio,
            "memory_ratio": size_ratio  # 内存减少与大小减少基本一致
        }